We are developing and evaluating statistical methods appropriate to prediction of protein structure from sequence. These methods include Fisher discriminant analysis, logistic discriminant analysis, artificial neural networks, density estimation techniques, cross-validation and bootstrap techniques, and computer graphical approaches. A new finding in this field is the apparent utility of homologous sequences in predicting the structure of an index sequence. An overall improvement of 4-5% is obtained using this approach, compared with others. We have sought to further increase the efficiency of these algorithms by optimizing the alignment of the homologous sequences, and by making use of ancillary information, such as the presence of gaps in the alignment. In a study that attempts to refute the notion of saltatory or pulsatile growth in humans, an analysis of daily length measurements in humans was made. A new analysis method was proposed that is more efficient than previous approaches, yet is easily interpreted graphically and provides a precise definition of a saltatory growth process. Numerical simulations confirmed the performance characteristics of the method. The statistical analysis of the relationships between placental corticotropin releasing hormone (CRH) and other hormones of the hypothalamic-pituitary-adrenal axis in third trimester pregnancy showed that, while adrenocorticotropin (ACTH) and cortisol are correlated over the 12 hour sampling period, CRH does not correlate significantly with ACTH or cortisol nor does it show circadian variation. Thus, there is no evidence of a regulatory role of glucocorticoids on placental CRH. Statistical and mathematical modeling consultation and advice were given to several NIH investigators in areas of ligand binding and kinetic data analysis. Refinement to the computer programs LIGAND and ALLFIT were made, especially in the area of the user interface and graphics. Several hundred copies of these programs were distributed to users at NIH and elsewhere.